Dasbor Situasi Coronavirus di Indonesia
Dasbor ini merupakan visualisasi data mengenai epidemi COVID-19 yang sedang berlangsung di Indonesia. Dasbor ini dikembangkan menggunakan piranti lunak R. Pengembangan dasbor ini diadaptasi dari dasbor yang dikembangkan oleh Rami Krispin.
Data
Data yang digunakan untuk pengembangan dasbor ini menggunakan data yang dikumpulkan oleh relawan KawalCovID19 dan juga dataset dari pustaka R {coronavirus}
Data akan selalu diupdate setiap hari.
Data mentah diambil dari data yang disediakan Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repositori.
Kontak
Untuk mengetahui kode sumber dasbor ini dapat mengunjungi Github.
---
title: "Status Coronavirus di Indonesia"
author: "Aldilas Achmad N."
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
#------------------ Packages ------------------
library(tidyverse)
library(flexdashboard)
library(plotly)
library(googlesheets4)
library(gargle)
library(curl)
library(networkD3)
library(igraph)
library(htmlwidgets)
library(htmltools)
library(sf)
library(leaflet)
library(leafpop)
#library(readxl)
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
numtest_color <- "orange"
#------------------ Data Indonesia---------------
# designate project-specific cache
options(gargle_oauth_cache = ".kawalcovidauth", gargle_oauth_email = "mail.aldilas@gmail.com")
gs4_auth(cache = ".kawalcovidauth")
df <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Statistik Harian") %>% rename(date = 1, case_daily = 2, case_cum = 5, recovered_daily = 8, recovered_cum = 9, death_daily = 12, death_cum = 13, test_daily = 25)
df$date <- as.Date(df$date, format = "%d %b")
#------------------ Data lokal pulau Jawa--------
provCaseCum <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", range = "Timeline!A1:AI129") %>% rename(date = 1)
provCaseDaily <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", range = "Timeline!A131:AI262", col_names = TRUE) %>% rename(date = 1)
provCaseDaily[is.na(provCaseDaily)] <- 0
provCaseDat <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Kasus per Provinsi", range = "Kasus per Provinsi!B2:H38")
provCaseDat[4,1] <- "Kepulauan Bangka Belitung"
provCaseDat[6,1] <- "Daerah Istimewa Yogyakarta"
provCaseDat[7,1] <- "Daerah Khusus Ibukota Jakarta"
#provRecoverCum <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Timeline", range = cell_rows(50:68)) %>% rename(date = 1)
#provDeathCum<- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Timeline", range = cell_rows(90:111)) %>% rename(date = 1)
idn <- read_sf("D:/Analisis Data Transaksi Halodoc/idn_map/Indonesia_Polygon.shp")
provCaseDat <- provCaseDat %>% pivot_longer(cols = -`Provinsi Asal`, names_to = "type")
provCaseMap <- merge(idn, provCaseDat, all = TRUE)
#Google Mobility data
mobility <- read_csv("Global_Mobility_Report.csv") %>% filter(sub_region_1 == "Special Region of Yogyakarta")
```
Statistik Nasional
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### Confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$case_daily, na.rm = T), big.mark = ","), "", sep = " "),
caption = "Total kasus terkonfirmasi",
icon = "fas fa-user-md",
color = active_color
)
```
### Recovered {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$recovered_daily, na.rm=TRUE), big.mark = ","), " (", round(100 * sum(df$recovered_daily, na.rm = TRUE) / sum(df$case_daily, na.rm=TRUE), 1), "%)", sep = ""),
caption = "Jumlah kasus sembuh",
icon = "fas fa-laugh",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death_daily, na.rm = TRUE), big.mark = ","), " (", round(100 * sum(df$death_daily, na.rm = TRUE) / sum(df$case_daily, na.rm = TRUE), 1), "%)", sep = ""),
caption = "Jumlah kasus meninggal",
icon = "fas fa-sad-tear",
color = death_color
)
```
### Num of Test {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$test_daily, na.rm = TRUE), big.mark = ","), "", sep = " "),
caption = "Jumlah tes yang telah dilakukan",
icon = "fas fa-vial",
color = numtest_color
)
```
Row {data-width=400}
-----------------------------------------------------------------------
### **Laporan kasus kumulatif**
```{r message=FALSE, warning=FALSE}
plot_ly(data = df, x = ~date) %>%
add_trace(
y = ~case_cum,
type = "scatter",
mode = "lines+markers",
name = "Jumlah kasus Terkonfirmasi",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
add_trace(
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Jumlah kasus kematian",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
add_trace(
y = ~recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Jumlah kasus sembuh",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
add_annotations(
x = as.Date("2020-03-11"),
y = 3,
text = paste("Kematian pertama"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 0,
ay = -90
) %>%
layout(
title = "",
yaxis = list(title = "Jumlah kasus kumulatif"),
xaxis = list(title = "Tanggal", range = c("2020-03-02", "2020-06-26")),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
### **Laporan kasus harian**
```{r message=FALSE, warning=FALSE}
plot_ly(data = df, x = ~date) %>%
add_trace(
y = ~case_daily,
name = "Kasus terkonfirmasi baru",
type = "bar",
marker = list(color = active_color)
) %>%
add_trace(
y = ~death_daily,
name = "Kasus meninggal",
type = "bar",
marker = list(color = death_color)
) %>%
add_trace(
y = ~recovered_daily,
name = "Kasus sembuh",
type = "bar",
marker = list(color = recovered_color)
) %>%
add_trace(
y = ~test_daily,
name = "Jumlah test yang dilakukan",
type = "bar",
marker = list(color = numtest_color)
) %>%
add_annotations(
x = as.Date("2020-03-16"),
y = 3,
text = paste("Perubahan kriteria pemeriksaan"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 0,
ay = -90
) %>%
layout(
title = "",
yaxis = list(title = "Jumlah kasus harian"),
xaxis = list(title = "Tanggal", range = c("2020-03-02", "2020-06-26")),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Statistik Provinsi
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### Perbandingan data kumulatif antar-provinsi di Pulau Jawa
```{r}
plot_ly(data = provCaseCum, x = ~date) %>%
add_trace(
y = ~Jakarta,
name = "DKI Jakarta",
type = "scatter",
mode = "lines+markers"
) %>%
add_trace(
y = ~Banten,
name = "Banten",
type = "scatter",
mode = "lines+markers"
) %>%
add_trace(
y = ~Jabar,
name = "Jawa Barat",
type = "scatter",
mode = "lines+markers"
) %>%
add_trace(
y = ~Jateng,
name = "Jawa Tengah",
type = "scatter",
mode = "lines+markers"
) %>%
add_trace(
y = ~DIY,
name = "DI Yogyakarta",
type = "scatter",
mode = "lines+markers"
) %>%
add_trace(
y = ~Jatim,
name = "Jawa Timur",
type = "scatter",
mode = "lines+markers"
) %>%
layout(
title = "",
yaxis = list(title = "Jumlah Kasus Kumulatif"),
xaxis = list(title = "Tanggal"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
### Perbandingan data harian antar-provinsi di Pulau Jawa
```{r}
plot_ly(data = provCaseDaily, x = ~date) %>%
add_trace(
y = ~Jakarta,
name = "DKI Jakarta",
type = "bar"
) %>%
add_trace(
y = ~Banten,
name = "Banten",
type = "bar"
) %>%
add_trace(
y = ~Jabar,
name = "Jawa Barat",
type = "bar"
) %>%
add_trace(
y = ~Jateng,
name = "Jawa Tengah",
type = "bar"
) %>%
add_trace(
y = ~DIY,
name = "DI Yogyakarta",
type = "bar"
) %>%
add_trace(
y = ~Jatim,
name = "Jawa Timur",
type = "bar"
) %>%
layout(
title = "",
yaxis = list(title = "Jumlah Kasus Kumulatif"),
xaxis = list(title = "Tanggal"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Google Mobility Data
=======================================================================
Row {data-width=400}
-------------------------------------
### Daerah Istimewa Yogyakarta
```{r}
plot_ly(data = mobility, x = ~date) %>%
add_trace(
y = ~retail_and_recreation_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Tempat rekreasi"
) %>%
add_trace(
y = ~grocery_and_pharmacy_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Perbelanjaan dan Farmasi"
) %>%
add_trace(
y = ~parks_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Taman"
) %>%
add_trace(
y = ~transit_stations_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Transportasi"
) %>%
add_trace(
y = ~workplaces_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Tempat kerja"
) %>%
add_trace(
y = ~residential_percent_change_from_baseline,
type = "scatter",
mode = "lines",
name = "Pemukiman"
) %>%
add_trace(
data = provCaseDaily,
x = ~date,
y = ~DIY,
type = "bar",
name = "Kasus Harian Baru"
) %>%
add_annotations(
x = as.Date("2020-06-07"),
y = 3,
text = paste("Berita viral Malioboro padat pesepeda"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 0,
ay = -90
) %>%
layout(
title = "Mobility Data Wilayah DIY Selama Pandemi",
yaxis = list(title = "Intensitas"),
xaxis = list(title = "Tanggal", rangeslider = list(type = "date"), range = c("2020-02-15", "2020-07-07")),
legend = list(x = 0.01, y = -0.0001),
hovermode = "compare"
)
```
Peta Kasus Seluruh Indonesia
=======================================================================
Row {data-width=400}
-------------------------------------
### **Jumlah kasus di seluruh dunia**
```{r message=FALSE, warning=FALSE}
cv_data_for_plot <- st_centroid(provCaseMap) %>%
dplyr::mutate(log_cases = 2 * log(value))
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("Kasus", "Kematian", "Sembuh"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
color = ~pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "value", "locname")
),
group = df,
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
Tentang Dasbor
=======================================================================
**Dasbor Situasi Coronavirus di Indonesia**
Dasbor ini merupakan visualisasi data mengenai epidemi COVID-19 yang sedang berlangsung di Indonesia. Dasbor ini dikembangkan menggunakan piranti lunak R. Pengembangan dasbor ini diadaptasi dari [dasbor](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} yang dikembangkan oleh Rami Krispin.
**Data**
Data yang digunakan untuk pengembangan dasbor ini menggunakan data yang dikumpulkan oleh relawan [KawalCovID19](https://kawalcovid19.id){target="_blank"} dan juga dataset dari pustaka R [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"}
Data akan selalu diupdate setiap hari.
Data mentah diambil dari data yang disediakan Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repositori](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.
**Kontak**
Untuk mengetahui kode sumber dasbor ini dapat mengunjungi [Github](https://github.com/aldeetropolis/flexboard-covid-id){target="_blank"}.